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Relevant bibliographies by topics / Cellular deconvolution

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Academic literature on the topic 'Cellular deconvolution'

Author: Grafiati

Published: 2 March 2024

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Journal articles on the topic "Cellular deconvolution"

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Main, Martin J., and Andrew X. Zhang. "Advances in Cellular Target Engagement and Target Deconvolution." SLAS DISCOVERY: Advancing the Science of Drug Discovery 25, no. 2 (2020): 115–17. http://dx.doi.org/10.1177/2472555219897269.

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Menden, Kevin, Mohamed Marouf, Sergio Oller, et al. "Deep learning–based cell composition analysis from tissue expression profiles." Science Advances 6, no. 30 (2020): eaba2619. http://dx.doi.org/10.1126/sciadv.aba2619.

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We present Scaden, a deep neural network for cell deconvolution that uses gene expression information to infer the cellular composition of tissues. Scaden is trained on single-cell RNA sequencing (RNA-seq) data to engineer discriminative features that confer robustness to bias and noise, making complex data preprocessing and feature selection unnecessary. We demonstrate that Scaden outperforms existing deconvolution algorithms in both precision and robustness. A single trained network reliably deconvolves bulk RNA-seq and microarray, human and mouse tissue expression data and leverages the combined information of multiple datasets. Because of this stability and flexibility, we surmise that deep learning will become an algorithmic mainstay for cell deconvolution of various data types. Scaden’s software package and web application are easy to use on new as well as diverse existing expression datasets available in public resources, deepening the molecular and cellular understanding of developmental and disease processes.

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Sosina, Olukayode A., Matthew N. Tran, Kristen R. Maynard, et al. "Strategies for cellular deconvolution in human brain RNA sequencing data." F1000Research 10 (August 4, 2021): 750. http://dx.doi.org/10.12688/f1000research.50858.1.

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Background: Statistical deconvolution strategies have emerged over the past decade to estimate the proportion of various cell populations in homogenate tissue sources like brain using gene expression data. However, no study has been undertaken to assess the extent to which expression-based and DNAm-based cell type composition estimates agree. Results: Using estimated neuronal fractions from DNAm data, from the same brain region (i.e., matched) as our bulk RNA-Seq dataset, as proxies for the true unobserved cell-type fractions (i.e., as the gold standard), we assessed the accuracy (RMSE) and concordance (R2) of four reference-based deconvolution algorithms: Houseman, CIBERSORT, non-negative least squares (NNLS)/MIND, and MuSiC. We did this for two cell-type populations - neurons and non-neurons/glia - using matched single nuclei RNA-Seq and mismatched single cell RNA-Seq reference datasets. With the mismatched single cell RNA-Seq reference dataset, Houseman, MuSiC, and NNLS produced concordant (high correlation; Houseman R2 = 0.51, 95% CI [0.39, 0.65]; MuSiC R2 = 0.56, 95% CI [0.43, 0.69]; NNLS R2 = 0.54, 95% CI [0.32, 0.68]) but biased (high RMSE, >0.35) neuronal fraction estimates. CIBERSORT produced more discordant (moderate correlation; R2 = 0.25, 95% CI [0.15, 0.38]) neuronal fraction estimates, but with less bias (low RSME, 0.09). Using the matched single nuclei RNA-Seq reference dataset did not eliminate bias (MuSiC RMSE = 0.17). Conclusions: Our results together suggest that many existing RNA deconvolution algorithms estimate the RNA composition of homogenate tissue, e.g. the amount of RNA attributable to each cell type, and not the cellular composition, which relates to the underlying fraction of cells.

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Diaz, Michael, Jasmine Tran, Nicole Natarelli, Akash Sureshkumar, and Mahtab Forouzandeh. "Cellular Deconvolution Reveals Unique Findings in Several Cell Type Fractions Within the Basal Cell Carcinoma Tumor Microenvironment." SKIN The Journal of Cutaneous Medicine 7, no. 6 (2023): 1170–73. http://dx.doi.org/10.25251/skin.7.6.15.

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Introduction: Despite therapeutic advancements, locally advanced and metastatic basal cell carcinomas continue to carry poor prognoses and high recurrence rates. Current treatment options remain suboptimal due to limited efficacy and associated adverse events. The objectives of this study are to 1) characterize the basal cell carcinoma immune cell microenvironment and 2) identify novel therapeutic targets. Methods: Transcriptome data representing 25 basal cell carcinoma and 25 control tissue samples were obtained from the Gene Expression Omnibus. Cell type fraction estimates were derived by least-squares deconvolution. Population differences were determined by Mann-Whitney U test. Results: Most significantly, two deconvolution algorithms similarly observed greater B cell infiltration in tumor samples compared to normal tissue (P<0.0001). Conclusion: Importantly, the results of this study provide new insight into the basal cell carcinoma tumor microenvironment and nominate testable immune cell populations for future therapeutic discovery. Study limitations include sample size and applicable background prediction levels of bulk deconvolution tools.

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Kim, Boyoung. "DVDeconv: An Open-Source MATLAB Toolbox for Depth-Variant Asymmetric Deconvolution of Fluorescence Micrographs." Cells 10, no. 2 (2021): 397. http://dx.doi.org/10.3390/cells10020397.

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To investigate the cellular structure, biomedical researchers often obtain three-dimensional images by combining two-dimensional images taken along the z axis. However, these images are blurry in all directions due to diffraction limitations. This blur becomes more severe when focusing further inside the specimen as photons in deeper focus must traverse a longer distance within the specimen. This type of blur is called depth-variance. Moreover, due to lens imperfection, the blur has asymmetric shape. Most deconvolution solutions for removing blur assume depth-invariant or x-y symmetric blur, and presently, there is no open-source for depth-variant asymmetric deconvolution. In addition, existing datasets for deconvolution microscopy also assume invariant or x-y symmetric blur, which are insufficient to reflect actual imaging conditions. DVDeconv, that is a set of MATLAB functions with a user-friendly graphical interface, has been developed to address depth-variant asymmetric blur. DVDeconv includes dataset, depth-variant asymmetric point spread function generator, and deconvolution algorithms. Experimental results using DVDeconv reveal that depth-variant asymmetric deconvolution using DVDeconv removes blurs accurately. Furthermore, the dataset in DVDeconv constructed can be used to evaluate the performance of microscopy deconvolution to be developed in the future.

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Turner, J. N., B. Roysam, T. J. Holmes, et al. "Visualization and quantitation of cellular and tissue anatomy by 3D light microscopy." Proceedings, annual meeting, Electron Microscopy Society of America 52 (1994): 928–29. http://dx.doi.org/10.1017/s0424820100172371.

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Many areas of biomedical research require the visualization and quantitation of 3D micro- and tissue level anatomy. Both are possible by utilizing confocal or wide-field light microscopy in combination with computational methods, including deblurring and 3D image analysis. The three-dimensionality of the specimen, required image resolution, and parameters to be quantitated dictate the methods of choice. Applications under study by our group range from automatically quantitating a few cell layers in cervical/vaginal smears, or hundreds of cell nuclei or immunologically labeled cells in thick brainslices, to tracing individual neurons for long distances in 3D space. Examples of our wide-field deconvolution methods and automated detection and counting of nuclei in thick tissue are shown.Image restoration by blind deconvolution of portions of the axonal fields of two rat hippocampal pyramidal cells injected with biocytin and contrasted with horseradish peroxidase and diaminobenzidine6 is shown in Figure 1. The original data is projected along and perpendicular to the optic axis in Fig. 1a while 1b is a similar set of restored image projections.

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Abbas, Alexander R., Kristen Wolslegel, Dhaya Seshasayee, and Hilary F. Clark. "Deconvolution of Blood Microarray Data Elucidates Cellular Activation Patterns in SLE." Clinical Immunology 123 (2007): S125—S126. http://dx.doi.org/10.1016/j.clim.2007.03.536.

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Udpa, L., V. M. Ayres, Yuan Fan, Qian Chen, and S. A. Kumar. "Deconvolution of atomic force microscopy data for cellular and molecular imaging." IEEE Signal Processing Magazine 23, no. 3 (2006): 73–83. http://dx.doi.org/10.1109/msp.2006.1628880.

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Blum, Yuna, Marie-Claude Jaurand, Aurélien De Reyniès, and Didier Jean. "Unraveling the cellular heterogeneity of malignant pleural mesothelioma through a deconvolution approach." Molecular & Cellular Oncology 6, no. 4 (2019): 1610322. http://dx.doi.org/10.1080/23723556.2019.1610322.

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Poirier, Christopher C., Win Pin Ng, Douglas N. Robinson, and Pablo A. Iglesias. "Deconvolution of the Cellular Force-Generating Subsystems that Govern Cytokinesis Furrow Ingression." PLoS Computational Biology 8, no. 4 (2012): e1002467. http://dx.doi.org/10.1371/journal.pcbi.1002467.

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Dissertations / Theses on the topic "Cellular deconvolution"

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Wang, Chuangqi. "Machine Learning Pipelines for Deconvolution of Cellular and Subcellular Heterogeneity from Cell Imaging." Digital WPI, 2019. https://digitalcommons.wpi.edu/etd-dissertations/587.

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Cell-to-cell variations and intracellular processes such as cytoskeletal organization and organelle dynamics exhibit massive heterogeneity. Advances in imaging and optics have enabled researchers to access spatiotemporal information in living cells efficiently. Even though current imaging technologies allow us to acquire an unprecedented amount of cell images, it is challenging to extract valuable information from the massive and complex dataset to interpret heterogeneous biological processes. Machine learning (ML), referring to a set of computational tools to acquire knowledge from data, provides promising solutions to meet this challenge. In this dissertation, we developed ML pipelines for deconvolution of subcellular protrusion heterogeneity from live cell imaging and molecular diagnostic from lens-free digital in-line holography (LDIH) imaging. Cell protrusion is driven by spatiotemporally fluctuating actin assembly processes and is morphodynamically heterogeneous at the subcellular level. Elucidating the underlying molecular dynamics associated with subcellular protrusion heterogeneity is crucial to understanding the biology of cellular movement. Traditional ensemble averaging methods without characterizing the heterogeneity could mask important activities. Therefore, we established an ACF (auto-correlation function) based time series clustering pipeline called HACKS (deconvolution of heterogeneous activities in coordination of cytoskeleton at the subcellular level) to identify distinct subcellular lamellipodial protrusion phenotypes with their underlying actin regulator dynamics from live cell imaging. Using our method, we discover “accelerating protrusion”, which is driven by the temporally ordered coordination of Arp2/3 and VASP activities. Furthermore, deriving the merits of ML, especially Deep Learning (DL) to learn features automatically, we advanced our pipeline to learn fine-grained temporal features by integrating the prior ML analysis results with bi-LSTM (bi-direction long-short term memory) autoencoders to dissect variable-length time series protrusion heterogeneity. By applying it to subcellular protrusion dynamics in pharmacologically and metabolically perturbed epithelial cells, we discovered fine differential response of protrusion dynamics specific to each perturbation. This provides an analytical framework for detailed and quantitative understanding of molecular mechanisms hidden in their heterogeneity. Lens-free digital in-line holography (LDIH) is a promising microscopic tool that overcomes several drawbacks (e.g., limited field of view) of traditional lens-based microscopy. Numerical reconstruction for hologram images from large-field-of-view LDIH is extremely time-consuming. Until now, there are no effective manual-design features to interpret the lateral and depth information from complex diffraction patterns in hologram images directly, which limits LDIH utility for point-of-care applications. Inherited from advantages of DL to learn generalized features automatically, we proposed a deep transfer learning (DTL)-based approach to process LDIH images without reconstruction in the context of cellular analysis. Specifically, using the raw holograms as input, the features extracted from a well-trained network were able to classify cell categories according to the number of cell-bounded microbeads, which performance was comparable with that of object images as input. Combined with the developed DTL approach, LDIH could be realized as a low-cost, portable tool for point-of-care diagnostics. In summary, this dissertation demonstrate that ML applied to cell imaging can successfully dissect subcellular heterogeneity and perform cell-based diagnosis. We expect that our study will be able to make significant contributions to data-driven cell biological research.

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Tai, An-Shun, and 戴安順. "Statistical Deconvolution Models for Inferring Cellular Heterogeneity." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/grdcvb.

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博士<br>國立清華大學<br>統計學研究所<br>107<br>Tumor tissue samples comprise a mixture of cancerous and surrounding normal cells. Inferring the cell heterogeneity of tumors is critical to the understanding of cancer prognosis and the treatment decisions. Compared with the experimental methods of using cell sorting technology to isolate cell components, in silico decomposition of mixed cell samples is faster and cheaper. The present study introduces three novel statistical approaches, CloneDeMix, BayICE, and PEACH, for different issues to perform the cellular proportion estimation as well as the genomic inference. First, CloneDeMix adopts clustering approach to dissect the tumor subclonal architecture induced by copy number aberration of genes through DNA sequencing data. Different from CloneDeMix analyzing cancerous cell populations, BayICE next estimates the components of tumor-infiltrating cells such as immune cells via a Bayesian framework with stochastic variable selection. Last, PEACH uses a penalized deconvolution model based on transcriptomic data to investigate the phenomenon that the genes of the particular cell types express inconsistently after cell sorting. These models were validated through simulated data and real data to demonstrate the performance of deconvolution. Furthermore, the analysis of cancer and immune-related diseases showed the results associated with biological interpretation. All of the works are implemented on the corresponding R packages which are publicly available to perform the deconvolution analysis.

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Chuang, Tony Chih-Yuan. "The three-dimensional (3D) organization of telomeres during cellular transformation." 2010. http://hdl.handle.net/1993/4228.

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Statement of Problem Telomere dynamics in the three-dimensional (3D) space of the mammalian nucleus plays an important role in the maintenance of genomic stability. However, the telomere distribution in 3D nuclear space of normal and tumor cells was unknown when the study was initiated. Methods Telomere fluorescence in situ hybridization (FISH) and 3D molecular imaging, deconvolution, and analysis were used to investigate telomere organization in normal, immortalized and tumor cells from mouse and human cell lines, and primary tissues. Results Telomeres are organized in a non-overlapping manner and in a cell-cycle dependant fashion in normal cells. In the late G2 phase of cell cycle, telomeres are assembled into a flattened sphere that is termed the telomeric disk In contrast, the telomeric disk is disrupted in the tumor cells. Moreover, telomeric aggregates (TAs) are found in tumor cells. Conditional c-Myc over-expression induces telomeric aggregation leading to the onset of breakage-bridge-fusion cycles and subsequent chromosomal abnormality. Conclusions Telomeres are distributed in a nonrandom and dynamic fashion in the 3D space of a normal cell. Telomeric aggregates are present in cells with genomic instability such as tumor cells and cells with deregulation of c-Myc. Consequently, TA can be a useful biomarker for research in cancer and other disease processes.

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Books on the topic "Cellular deconvolution"

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Clive, Standley, Hughes John, and United States. National Aeronautics and Space Administration., eds. Iterative deconvolution of X-ray and optical SNR images. National Aeronautics and Space Administration, 1992.

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Book chapters on the topic "Cellular deconvolution"

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Howell, Gareth, and Kyle Dent. "Bioimaging: light and electron microscopy." In Tools and Techniques in Biomolecular Science. Oxford University Press, 2013. http://dx.doi.org/10.1093/hesc/9780199695560.003.0017.

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This chapter discusses the use of light and electron microscopy techniques to image biological structures. Bioimaging enables the visualization of different biological processes such as protein transport, the development or effect of disease, and mutations on a cellular and subcellular scale, and provides structural information on cells, organelles, and individual macromolecular complexes. The chapter looks at the technologies commonly used in studying cells and protein structures such as confocal microscopy, deconvolution microscopy, transmission electron microscopy, and scanning electron microscopy. It describes the basic structure of different microscopes and gives an overview of how images are generated and visualized. Furthermore, the discussion covers the common applications of these microscope technologies in modern biological research. It also explores correlative light electron microscopy, a technique that combines fluorescence microscopy and transmission electron microscopy to produce high-resolution images that include localized fluorescence information specific to the protein of interest.

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Marks II, Robert J. "Signal and Image Synthesis: Alternating Projections Onto Convex Sets." In Handbook of Fourier Analysis & Its Applications. Oxford University Press, 2009. http://dx.doi.org/10.1093/oso/9780195335927.003.0016.

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Alternating projections onto convex sets (POCS) [319, 918, 1324, 1333] is a powerful tool for signal and image restoration and synthesis. The desirable properties of a reconstructed signal may be defined by a convex set of constraint parameters. Iteratively projecting onto these convex constraint sets can result in a signal which contains all desired properties. Convex signal sets are frequently encountered in practice and include the sets of bandlimited signals, duration limited signals, causal signals, signals that are the same (e.g., zero) on some given interval, bounded signals, signals of a given area and complex signals with a specified phase. POCS was initially introduced by Bregman [156] and Gubin et al. [558] and was later popularized by Youla & Webb [1550] and Sezan & Stark [1253]. POCS has been applied to such topics as acoustics [300, 1381], beamforming [426], bioinformatics [484], cellular radio control [1148], communications systems [29, 769, 1433], deconvolution and extrapolation [718, 907, 1216], diffraction [421], geophysics [4], image compression [1091, 1473], image processing [311, 321, 470, 471, 672, 736, 834, 1065, 1069, 1093, 1473, 1535, 1547, 1596], holography [880, 1381], interpolation [358, 559, 1266], neural networks [1254, 1543, 909, 913, 1039], pattern recognition [1444, 1588], optimization [598, 1359, 1435], radiotherapy [298, 814, 1385], remote sensing [1223], robotics [740], sampling theory [399, 1334, 1542], signal recovery [320, 737, 1104, 1428, 1594], speech processing [1450], superresolution [399, 633, 654, 834, 1393, 1521], television [736, 786], time-frequency analysis [1037, 1043], tomography [1103, 713, 1212, 1213, 1275, 916, 1322, 1060, 1040], video processing [560, 786, 1092], and watermarking [19, 1470]. Although signal processing applications ofPOCS use sets of signals,POCSis best visualized viewing the operations on sets of points. In this section, POCS is introduced geometrically in two and three dimensions. Such visualization of POCS is invaluable in application of the theory.

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Conference papers on the topic "Cellular deconvolution"

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Eisenberg, Marisa, Joshua Ash, and Dan Siegal-Gaskins. "In silicosynchronization of cellular populations through expression data deconvolution." In the 48th Design Automation Conference. ACM Press, 2011. http://dx.doi.org/10.1145/2024724.2024906.

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Mir, Mustafa, S. Derin Babacan, Michael Bednarz, Minh N. Do, Ido Golding, and Gabriel Popescu. "Imaging sub-cellular structures using three-dimensional sparse deconvolution SLIM." In Biomedical Optics. OSA, 2012. http://dx.doi.org/10.1364/biomed.2012.bm4b.2.

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Rathnayake, S., B. Ditz, J. Van Nijnatten, et al. "Influence of smoking on bronchial epithelial cell composition by cellular deconvolution and IHC." In ERS International Congress 2022 abstracts. European Respiratory Society, 2022. http://dx.doi.org/10.1183/13993003.congress-2022.666.

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Chen, Li, peter Choyke, Robert Clarke, Zaver Bhujwalla, and Yue Wang. "Abstract A10: Unsupervised deconvolution of dynamic imaging reveals intratumor vascular heterogeneity and repopulation dynamics." In Abstracts: AACR Special Conference on Cellular Heterogeneity in the Tumor Microenvironment; February 26 — March 1, 2014; San Diego, CA. American Association for Cancer Research, 2015. http://dx.doi.org/10.1158/1538-7445.chtme14-a10.

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Adams, T., J. C. Schupp, J. E. McDonough, et al. "Deconvolution of Bulk RNAseq Datasets Confirms Substantial Cellular Population Shifts in the Distal Lung in IPF." In American Thoracic Society 2020 International Conference, May 15-20, 2020 - Philadelphia, PA. American Thoracic Society, 2020. http://dx.doi.org/10.1164/ajrccm-conference.2020.201.1_meetingabstracts.a2248.

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Miheecheva, Natalia, Maria Sorokina, Akshaya Ramachandran, et al. "Abstract 161: Evaluating the clinical utility of RNA-seq-based PD-L1 expression and cellular deconvolution as alternatives to conventional immunohistochemistry in clear cell renal cell carcinoma." In Proceedings: AACR Annual Meeting 2021; April 10-15, 2021 and May 17-21, 2021; Philadelphia, PA. American Association for Cancer Research, 2021. http://dx.doi.org/10.1158/1538-7445.am2021-161.

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